An experimental examination of fisheries with concurrent common pool and individual quota management.
Anderson, Christopher M. ; Uchida, Hirotsugu
I. INTRODUCTION
How user communities exploit and develop institutions to manage
common pool resources attracts attention from across the social sciences
(e.g., National Research Council 2002). Dynamic common pool resources
such as aquifers, forest soils, and fisheries are a focus because of
their potential to sustain income, and provide food and water security,
particularly in traditional communities, and in vulnerable communities
in the developing world (e.g., Arnason, Kelleher, and Willman 2009;
Baland and Platteau 1996). Economic analyses have been directed
primarily at identifying extraction levels that central planners can
impose in order to maximize the highest sustainable flow of resource
benefits (Conrad 2010; Wilen 2000), while political scientists,
sociologists, and anthropologists have studied the governance structures
that establish use norms and policies, and the conditions under which
users can successfully manage the resource without central intervention
(e.g., Agrawal 2002; Ostrom 2009).
Recent trends in management have introduced economically
significant examples of dynamic resources where a central planner
constrains the full extent of the interseason externality, ensuring
sustainable resource levels and extractions, but other externalities
persist within seasons that have the potential to dissipate much of the
rent. The most prevalent examples are in fisheries, where management
agencies use biological fish stock assessments to establish a hard
seasonal total allowable catch (TAC) for each fishery to ensure stock
health. In many fisheries, "catch share" programs allocate
shares of the TAC as quota to groups, or to individuals who then can or
must form cooperatives, to manage intraseason externalities caused by
concurrent production congestion, in-season growth patterns, or market
timing. Interest is added because these groups, called sectors in many
fisheries, are often self-identifying, and multiple sectors participate
in each fishery; harvesters can affiliate around harvesting methods,
management preferences, geography, or any other factor. On the one hand,
this strategy could be a method for carving users into groups that meet
Ostrom's (2009) conditions and thus supporting self-management of
intraseason externalities (e.g., Cunningham and Bostock 2005; Aoki and
Hayami 2001; Townsend, Shotton, and Uchida 2008; Wilson, Nielsen, and
Dengbol 2003); on the other hand, it could be a technique for breaking
political gridlock preventing more efficient economic management of the
inter- or intraseason externalities (e.g., Acheson and Gardner 2011;
Kuperan and Sutinen 1998).
While such sectorization programs are thought to have many benefits
such as an increase in legitimacy, incorporating local knowledge, and
getting managers out of the business of setting individual allocations,
prospective and retrospective economic analysis of these policies is
difficult because, if different sectors choose different regulations,
the fishery can be regulated by multiple management systems
concurrently. This presents two significant challenges to analysts
assessing the effects of such catch share programs. First, they must
figure out not only how harvesters in each sector will respond to the
harvesting strategies of others in their sector but also how the sectors
will strategize against one another. Where contemporaneous congestion or
price externalities are present, some sectors' rules might permit
members to strategically adjust their harvesting behavior to respond to
harvesting strategies of other sectors. Thus, a theoretical and
empirical framework requires harvesters to make daily decisions that are
subject to cumulative annual constraints within a dynamic game, with
different types of players reflecting each sector. In addition, the
counterfactual policy environment against which the sector program will
be assessed must take into account the shifts in targeting and market
timing behavior that not only reflect changes in the rules that apply to
sector members but also the different rules under which harvesters are
operating.
A second analytical challenge is that, since all harvesters hold a
license to the same fishery regardless of the sector they join,
harvesters can typically change sectors or whole sectors can change how
they manage themselves. Thus, a forward-looking analysis of the sector
program needs to anticipate how the rules and practices in the fishery
might evolve over time, as harvesters gain experience under one set of
rules and observe outcomes under other sets of rules; those in less
successful sectors might migrate to sectors that are more successful. To
the extent that success is determined by the management rules each
sector selects for its catch allocation, this may prove to be an
evolutionary selection process for management rules.
Understanding how differently managed groups pursuing the same
stock will react is not entirely new in fisheries. Neighboring nations
will often manage transboundary stocks slightly differently, but these
situations typically result in TAC-setting strategies that reflect a
common pool resource management problem between governments (e.g., Grand
Banks fish wars, Levhari and Mirman 1980; northeast Atlantic mackerel,
Hanneson 2013 and Hotvedt 2011). Similarly, recreational anglers
frequently find themselves differently managed than commercial
harvesters, but the differences are driven by differences in objectives
and production functions, not designed to encourage one group to respond
to others.
What distinguishes our cases of interest is that a single
management framework allocates shares of a single TAC among groups of
users to allow different harvesters to pursue different management. This
creates a situation where at least one of the groups can choose
management that gives itself flexibility in pursuing their share in
strategic response to the others. An early example arose under the
Pacific Halibut Commission, where the jurisdictional boundary between
Canada and the United States allowed flexibility for each country to
select separate rules for harvesting their share of the Commission-set
TAC. British Columbia (BC) established an individual transferable quota
(ITQ) system that allowed harvesters to suspend landings during the
Alaskan derby, when prices were low (Casey et al. 1995); importantly,
Alaska later adopted an ITQ scheme, changing the value of the Canadian
ITQ system to BC harvesters. A current similar example arises from the
Atlantic States Marine Fisheries Commission's management of striped
bass by allocating portions of the coastwide TAC to states. In Virginia,
striped bass harvesters developed an ITQ system in order to extend the
season, which allows them to avoid market gluts caused during
neighboring North Carolina's few-day derby fishery (Crosson 2011).
Increasingly, however, single management bodies are incorporating
some form of sector formation within the catch share frameworks within a
single jurisdiction. For example, the Chignik Coop among salmon
harvesters in the Aleutian Islands of Alaska saw 77 of 99 harvesters
elect to join a sector designed to minimize harvesting costs by limiting
the number of members who fished while sharing revenue, while the rest
chose to remain in a common pool derby fishery (Deacon, Parker, and
Costello 2008; Knapp 2008). In Rhode Island, the eight vessels of the
Rhode Island Fluke Conservation Cooperative (Scheld, Anderson, and
Uchida 2012) accepted a hard TAC in exchange for a quota allocation of
summer flounder. (1) They elected to harvest around half of their quota
allocation when the common pool fluke fishery--managed as seasonal
common pool derbies--was closed, and sector members were the only
harvesters allowed to land fluke. Nearby, the Cape Cod Hook sector
(Pinto da Silva and Kitts 2006; Verani 2007) and the Cape Cod Fixed Gear
Sector (Georges Bank Cod Fixed Gear Sector 2010) hold collective
allocations of Northeast Multispecies fish that they can harvest
independent of many of the regulations that dominate the rest of the
fishery. In each of these cases, the sectors were established in order
to exploit opportunities for adjusting harvest timing from that required
by the governing management plans in order to receive better prices.
On the basis of the experience with the Cape Cod sectors, and the
small concurrent effort in Rhode Island, the entire Northeast
Multi-species Fishery adopted sector management in 2010, leading to the
establishment of 17 different self-identifying sectors that received
collective quota allocations for the included stocks to manage in any
way they wish, and a common pool that fishes competitively for the
remaining quota associated with their history. While sectors formed
primarily along gear, target species, and geographical lines, they were
able to adjust the timing of their participation in the groundfish
fishery to increase the landings and revenue from groundfish and
non-groundfish species each vessel pursues under different management
plans (Kitts et al. 2011). To begin to develop and test frameworks for
understanding what happens with single fisheries under multiple
management systems, we develop a controlled economic experiment to
demonstrate the strategic types of harvest timing changes that occur in
a fishery with two management systems: one sector that operates as a
common pool (CP), and one that operates using individual quotas (IQ)--a
common implementation of an efficiently managed sector with strong
property rights. (2) In a novel quasi-continuous fishery game with a
contemporaneous price externality, subjects individually choose how much
effort to exert in each week of a 52-week fishing season, knowing that
prices will be lower in weeks when total landings are higher. After
subjects gain experience in a CP fishery, an IQ fishery, and a fishery
where half the subjects are CP managed and half are IQ managed, they are
given the opportunity to choose which management system they prefer, and
fish for several seasons under their chosen management.
The next section presents the model that is the basis for our
experiment. Then, the experimental environment and treatments are
explained. Section IV presents the results, and Section V concludes with
an emphasis on how harvesting strategies in multiply managed common pool
resources can be importantly different from those where all harvesters
fall under the same set of management incentives.
II. THE FISHERY ENVIRONMENT
Game theoretic models of common pool resources that have been
widely studied are predominantly one-shot, static games in which players
choose a level of extraction or investment effort (e.g., Walker,
Gardner, and Ostrom 1990). Aggregate payoffs are inversely parabolic in
total effort, but increasing private payoffs conditioned on a level of
total effort lead to predicted Nash equilibrium extraction levels that
are well above the social optimum. In experimental tests, Nash
predictions are broadly consistent with the data in formulations
capturing cost-based congestion externalities motivated by groundwater
pumping (Gardner, Moore, and Walker 1997) and other production
externalities; in cases with dynamic externalities, the observed
outcomes can be even less efficient than the Nash prediction (Walker and
Gardner 1992).
These games are not well suited to study seasonal quotas in
fisheries because they emphasize the total levels of extraction within a
harvest period. In a fishery context, this corresponds to the problem of
setting the annual TAC, or harvesters simultaneously choosing their
annual landings. This abstracts from the incentives that are most
directly affected by the rules of catch share systems: the distribution
of effort across individuals (allocations) and the distribution of
effort across time. Thus, we use a common pool resource model wherein
agents choose their level of fishing effort in a number of periods
during a fishing season, subject to constraints on total harvest. This
model is similar to Fell (2009), but has fewer dynamic processes to
emphasize the market externality, simplify explanation to experimental
subjects, and facilitate closed form solutions.
Consider a common pool resource which can be extracted through a
season of t = 1, ..., T periods by i = 1, ..., I users. In each period,
each user chooses an effort [e.sub.it], leading to total effort
[E.sub.t] = [[SIGMA].sub.i] [e.sub.it], and the resulting total harvest
H([E.sub.t]). Then, the payoff in period t to a user choosing [e.sub.it]
is given by:
(1) [pi]([e.sub.it], [E.sub.t]) = [a - bH([E.sub.t])] x
[([e.sub.it]/[E.sub.t]) H([E.sub.t])] - [gamma][e.sub.it].
The first term in square brackets is the price, with a linear
demand curve that slopes down proportional to the total harvest at time
t if b > 0. The second bracketed term is i's share of the total
harvest, proportional to her share of the total effort. A production
externality can be imposed by specifying a concave function for H(*).
Finally, the rightmost term captures the linear cost associated with the
chosen effort level. In this article, we specify a linear H(*) and focus
on a price externality by setting b > 0. (3)
User i will choose a vector of [e.sub.it] that will be an
individual profit-maximizing best response to the [e.sub.jt] choices of
other users, subject to the constraints imposed by management. This
article tests hypotheses arising from the game theoretic model when one
group of harvesters of a common pool resource is managed through
aggregated quota (TAC) and another is managed with private harvesting
rights or IQ. In this environment, each group of harvesters must respond
not only to the strategies of the other users facing their management
incentives but also to the strategies of harvesters in the other
management system. Note that the total number of harvesters (i.e., the
sum of the two groups) is assumed fixed.
A. Single Rule Management
As a baseline for understanding the effects of having multiple
concurrent management systems within a fishery, we characterize
predictions for environments where all harvesters face the same
management incentives. We set up a simple model where each individual
harvester will maximize the total profit within a single fishing season,
subject to the management system she/he faces. Two additional
assumptions are made to simplify the model: first, we assume zero
discount rate because this is a within-season model, following previous
studies such as Boyce (2001) and Homans and Wilen (1997). Second, we
abstract away from any stock effect on the harvesting function. With
these assumptions, our maximization problem becomes a series of static
optimizations throughout the fishing season, similar to the model
described in Boyce (2001). (4) This simple model allows us to focus on
the essential hypotheses that we later test in the experiment, namely
the choice of effort level and harvest timing due to the difference in
management systems.
Harvesters within an IQ management system plan their effort levels
throughout a season of maximum length [T.sub.m] to solve the following
problem:
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
With IQ, harvesters have a secure claim to a total seasonal
quantity of harvest, [[bar.q].sub.i], and wish to time their landings so
as to sell them at the highest price.
While the season length T is one of the choice variables, it can be
shown that any season length shorter than the maximum allowed (i.e.,
[T.sup.*] < [T.sub.m]) is not part of the solution to this
optimization problem (see Supporting Information, Appendix SI). This is
to be expected, as in this environment the Nash equilibrium occurs when
aggregate landings are at the same level in every period t: if landings
are lower in one period than another, a harvester could receive a higher
price by shifting her landings from a higher total landings period to
the lower total landings period. (5) Thus, the IQ outcome is to stretch
the season to its full length, landing the same amount in each period
and attaining the efficient level of profit.
The interesting case is one where fishing is profitable such that
the quota is a binding constraint on total effort. In this environment,
and assuming identical individuals, the optimal harvest in each period
is simply
(3) H([E.sup.*]) = ([N[bar.q]]/[T.sub.m]),
which in principle can be solved for optimal aggregated fishing
effort, [E.sup.*].
The last case to consider is where even with the highest possible
fishing effort, while still profitable, allocated quota is not exhausted
before the season ends. This may occur, for example, if the maximum
season length is significantly short compared to the amount of quota
allocated. In this case, the optimal harvest volume and hence fishing
effort can be solved from the following equation
(4) (1/N)[(a-2bH(E))(dH/dE)] + (N - 1/N) (a - 2bH (E)) x (H(E)/E) -
[gamma] = 0.
One can easily verify that when N = 1 then Equation (4) becomes
marginal revenue equal marginal cost, i.e., the sole owner solution.
When N [right arrow] [infinity]. Equation (4) reduces to the open access
solution where average revenue equals average cost.
In summary, IQ harvesters will always spread their fishing effort
such that they operate during the entire season, i.e., [T.sup.*] =
[T.sub.m]. If the season length is long enough compared to the quota
allocated then each period's fishing effort will be as depicted in
Equation (3). Otherwise the fishing effort will be as depicted in
Equation (4), which is higher and can approach the derby fishing level
depending on the group size (N).
When all harvesters are managed by a CP, the maximization problem
is nearly identical to Equation (2) except for the first constraint,
which becomes
(5) T [N.summation over (i=1)] ([e.sub.i]/E) H(E) [less than or
equal to] [bar.Q],
reflecting that the total harvest among all harvesters cannot
exceed the TAC ([bar.Q]), but without limiting the total harvest of any
individual. The Nash equilibrium of this game is for each harvester to
harvest as intensively as is economically feasible (until marginal
revenue per unit effort reaches [gamma], or until [e.sub.max]) until the
quota is exhausted, a fishing derby. Because landings are high in each
period until the fishery closes, prices are low and the return to the
resource is below the social optimum.
B. Multiple Rule Management
When some of the harvesters are managed by IQ and others are
managed through a CP, each harvester must respond not only to the
incentives of her own management system but also the behavior of
harvesters in the other system. The incentives for the CP harvesters
have not changed: lacking an ability to secure harvest by any means
other than landing it, in equilibrium, CP harvesters engage in a full
effort fishing derby until their quota is exhausted and they are shut
down.
The IQ harvesters, however, can respond to the derby among CP
harvesters. As shown in Equation (3) from the model, the IQ harvesters
will adjust their landing in each period depending on the total season
length available to them ([T.sub.m]), provided that [T.sub.m] is long
enough to exhaust their quotas in that time. If [T.sub.m] is long
enough--i.e., available quotas of the CP group are such that IQ
harvesters can land less in every week remaining after CP shutdown than
the CP did during its derby--then IQ harvesters will not fish during the
derby and instead hold their quota to evenly distribute among the
remaining periods once the CP is closed. This is driven by profit in
each period being a decreasing function of the total effort of fishermen
operating that period, and the result is relatively clean in our model
because there is no discounting or stock effect on harvest productivity,
and thus there is no opportunity cost of waiting until the derby fishing
ends. Indeed, Scheld et al. (2012) found members of the RI Fluke
Conservation Cooperative reduced their fluke harvest during derbies,
choosing instead to land a large portion of their quota after the
closure of the Summer subseason, when no one else was permitted to land
and prices were 20% higher.
For the purpose of experimental design, however, we want to avoid
the situation where the season length remaining after the CP harvesters
exhaust their TAC is too short for IQ harvesters to harvest their entire
quotas. In such case Equation (4) will prevail, and the difference in
effort choice between CP and IQ harvesters becomes murky. On the basis
of our model, we chose the parameter values so that CP harvesters will
exhaust their aggregate TAC quickly enough so that the remaining periods
will be sufficient for IQ harvesters to catch all their IQ at the effort
level as shown in Equation (3).
III. EXPERIMENTAL DESIGN
To test these hypotheses about the effects of having multiple
management systems operating within the same fishery, we designed a
controlled economic experiment in which human subjects play the role of
harvesters of a common pool resource being managed concurrently by CP
and IQ. Each CP resource game represents a season, executed in 52
"weekly" effort decisions of the number of days in that week
to fish, [e.sub.it] [member of]{0,1, ..., 7}. Each of 12 subjects has
3,000 pounds of quota associated with her, and each day of effort leads
to landing 30 pounds of fish, [H.sub.t] ([E.sub.t]) = 30[E.sub.t].
Prices are determined by a downward sloping demand curve based on total
weekly landings of all subjects (in Equation (1) above, a = 50 and b =
0.012).
Each experimental session included playing 15 seasons, of which
three were randomly selected for payment at the end of the session. Each
season was conducted in one of four treatments: In the CP treatment, all
subjects are managed as a common pool, so any subject could fish as much
as she/he wished, until the 36.000 pounds of aggregate quota was
reached. In the IQ treatment, each subject was allocated 36,000 pounds
of quota to harvest whenever she/he wished. In the Mixed treatment, six
subjects were assigned to CP management (and would all be shut down once
the total of CP subject landings reached 18,000 pounds) and six were
assigned to IQ management, each assigned 3,000 pounds of IQ. In the
Choice treatment, subjects were asked prior to the beginning of each
season whether they wished to be in CP or IQ management: subjects
choosing IQ received a 3,000 pound quota allocation, and subjects
choosing CP had the 3,000 pounds associated with them placed in a common
pool, which was shared among all subjects choosing group limit. (6)
Given these parameters, the Nash equilibrium strategies for players
under each management system are presented in Table 1. In the CP
treatment, all 12 subjects fish as intensively as possible to secure
their share of the harvest, 7 days per week for the first 14 weeks,
leaving them each only 2 days remaining, which is expended in Week 15.
In the IQ treatment, the price externality is minimized by averaging
1.92 days per week for the entire season. In the Mixed treatment, the CP
subjects race-to-fish as in the CP treatment. The IQ subjects would
receive very low prices if they harvested during this derby, and thus
they hold their quota until CP subjects are forced to reduce their
effort, equating total landings--and thus price--across the remainder of
the season.
[FIGURE 1 OMITTED]
In five sessions, there was a series of three CP-managed seasons,
followed by three IQ-managed seasons, followed by four Mixed seasons
(permuting assignments so each subject was in each group twice), and
finally five Choice seasons. In the other four sessions, the first three
seasons were IQ managed, followed by three CP-managed seasons in advance
of the four Mixed and five Choice seasons. The last session of each
group was of experienced subjects, who had participated in one of the
earlier experiments (in either treatment ordering).
Figure 1 shows the interface of an IQ-managed subject in the Mixed
treatment. The center of the screen shows that this subject is in the
"Individual Limit" group, which has a total of 36,000 pounds
of quota allocated, including information on how much others in the IQ
group harvested the previous week and how much quota is remaining. To
the left is the individual subject's You box (not shown to
CP-managed subjects), which displays the subject's IQ allocation,
previous week harvest, and quota remaining. To the right is the Other
Group box, which shows the total quota allocation, previous week
harvest, and quota remaining of the other group. The graph above the
boxes tracks the subject's profit, and shows the quota remaining in
each group. The Profit box in the lower right shows the detailed profit
calculation, including individual landing times the prevailing price in
the previous week.
Subjects make their decisions by clicking a radio button,
corresponding to the "Number of days fishing per week" in the
lower left-center of the screen. A novel feature of this environment is
that it is quasi-continuous: weeks elapse every 4 s, capturing the
effort level in the Days radio button panel, so subjects focus on when
to change their effort level in response to their declining quota
balance, the landings of others, and the amount of time remaining. This
design has the advantage of allowing subjects a great deal of control
over how to divide up the use of their 100 fishing days worth of quota,
and an opportunity to respond to how others are using theirs. We are not
aware of previous continuous CP resource experiments, but evidence from
continuous time public goods experiments (e.g., Goren, Kurzban, and
Rapoport 2003) suggests that continuous time may facilitate coordination
on higher contributions to the public good, and shift outcomes slightly
toward the social optimum.
[FIGURE 2 OMITTED]
IV. RESULTS
Figure 2 shows the predicted and average observed effort levels in
each week in the last season of each treatment within a session, in the
CP (top panel), IQ (middle panel), and Mixed (bottom panel) treatments.
While the data do not precisely align with the predicted effort paths,
key features of the predictions are present in the data that suggest the
theoretical model is a useful organizing framework for the observed
behavior. In the CP-managed fishery, the Nash equilibrium is for all
subjects to choose the maximum possible effort level (7 in Weeks 1-14, 2
in Week 15), until the fishery is closed in Week 16. In the data,
average effort levels are somewhat below the maximum effort level, and
thus fishing continues for more weeks than predicted, but the level of
effort is still quite high and in most sessions the season ends before
Week 20. Under IQ management, Nash equilibrium predicts subjects will
distribute their effort, and therefore their landings, equally
throughout the season, so as to maximize the price received; this occurs
at an average effort level just below 2. The data start slightly above
2, and gradually decrease toward 2 after Week 40, falling below 2 as
some subjects exhaust their quotas due to higher initial effort levels.
However, the overall impression is that effort is reasonably well
distributed throughout the season, consistent with the prediction. In
comparing the CP and IQ treatments, it is apparent that CP management
induced a derby that led to much higher levels of effort and earlier
closing, while IQ management reduced effort levels and lengthened the
season considerably.
While the predictions in the CP and IQ treatments--where all
subjects face the same management incentives--are consistent with the
data, the Mixed treatment makes the more subtle prediction that IQ
subjects will shift their strategy in response to the behavior of the CP
group. The Nash equilibrium in the Mixed treatment for the CP-managed
subjects is the same as in the CP treatment, a derby at full effort
until the collective quota is exhausted. The IQ-managed subjects are
predicted to avoid the low prices during the derby, and hold their quota
until the CP fishery closes, when they then distribute their effort
evenly across the balance of the season. The CP subjects' data are
as consistent with the prediction as in the CP treatment, clearly
reflecting a derby. While some IQ subjects do not delay their harvesting
until the CP subjects are shut down, many do, and after the CP closure
there is a notable increase in effort--to roughly the predicted
level--among IQ subjects, which persists for the balance of the season.
The next section establishes the statistical significance of the data
patterns observed in Figure 2.
A. Single Management Systems
Table 2 presents the results of a two-level mixed-effects
regression (incorporating season and session random effects) of total
observed effort in all seasons of the CP and IQ treatments, as a
function of treatment and experience indicators, and week-of-season time
trend. The regression excludes observations in the first 3 weeks, the
last 3 weeks, and the weeks of and following closure.
The Constant in the regression reflects effort levels in the
CP-managed fishery, when it is open. Effort levels begin at 61.94, which
is significantly below the predicted effort of 84 (7 times 12 subjects).
However, the significantly positive coefficient on Week reflects that
effort increases over time as the derby intensifies, possibly reflecting
the collapse of initial efforts to coordinate on slower harvesting and
higher prices.
The IQ variable reflects the difference between average effort in
the CP and IQ treatments; it indicates average effort in the IQ
treatment is 32.97, roughly half the average effort in the CP fishery
(statistically different with p < [10.sup.-95]) and comparatively
close to the predicted value of 23 (1.92 times 12 subjects). The
significantly negative IQ x Week interaction, that is roughly twice the
size of the Week variable, reflects a decrease in effort intensity over
time in the IQ treatment. Efforts are nearly flat in the experienced
session's IQ treatment, where they begin at 17.27 fishing days. A
likelihood ratio test rejects the hypothesis of equal average efforts in
the CP and IQ treatments with p < [10.sup.-56].
As a result of the higher effort in CP, the CP treatment seasons
were shorter, as shown in Figure 3 (Wilcoxon rank-sum p = .002). The CP
group, whether in the single-management or mixed-management treatment,
exhausts its aggregated quota by around Week 19-21, while the IQ group
nearly always extends its season to the maximum. With high weekly
harvests, CP subjects' average profits were only $24,963, as
compared with $71,852 in the IQ treatment (Wilcoxon signed-rank p <
[10.sup.-18]). Thus, when all subjects are in the same management
system, we do observe outcomes consistent with the general predictions
that CP management leads to a low-profit derby and that IQ management
leads to higher profits throughout a longer season.
[FIGURE 3 OMITTED]
B. Multiple Concurrent (Mixed) Management Systems
Table 3 presents the results of seemingly unrelated regressions of
total effort among CP subjects (Equation (1)) and IQ subjects (Equation
(2)), with bootstrapped standard errors with resampling by season to
account for the nonindependence of observations from the same session.
The independent variables are Week and Experience as in Table 2, with
the addition of a CP_Closure variable that takes on a value of 1 if the
CP-managed subjects have been closed in the week from which the
observation is taken, and zero otherwise. In the CP equation, CP_Closure
interactions offset the Constant, Week, and Experience variables so the
predicted effort will be exactly zero when the fishery is closed; in the
IQ equation, CP_Closure interactions show how IQ-managed subjects
respond to the closure.
The only significant variable in the CP equation is Constant which,
at 31.77, is lower than the predicted level of 42 (7 days times 6
subjects) and does not change with Week. In response to this
nevertheless high and constant level of effort by the CP-managed
subjects, the IQ-managed subjects put forth an average of only 10.96
days of effort prior to the CP closure; this is reduced to 4.36 among
experienced subjects, close to the predicted effort level of zero
(though a likelihood ratio test gives p = .015). Once the CP-managed
subjects are closed, the IQ subjects' effort doubles, consistent
with the Nash equilibrium, increasing to 22.40 among inexperienced
subjects and 29.62 among those with experience. The insignificance of
the Week terms in the IQ regression means total IQ subject effort
remains constant for the balance of the season. Thus, while there is an
overall tendency toward more moderate effort levels than theory
predicts, the key dynamic predictions of the model are observed within
the data.
As in the single management system treatments, the differences in
effort levels induce statistically and economically significant
differences in profitability. Subjects managed by CP in the Mixed
treatment averaged $55,555 in profit, while those managed by IQ averaged
$73,936 (Wilcoxon signed-rank p < [10.sup.-13]). Thus, having two
management systems led to a harvest timing pattern consistent with that
predicted by the model, with the resulting predicted differences in
profit.
C. Management System Choice
As harvesters gain experience in, and observe outcomes in, the
alternative management systems through successive seasons, they learn
about their harvest and profit opportunities under each management
system, and may develop preferences for one system or the other based on
profitability or other properties. Having experienced outcomes under CP
management alone, IQ management alone, and under each management system
when competing against an equal number of subjects in the other, we
measured these preferences by offering subjects the choice of the
management system in which they would like to participate. The
experiment parameters ensured Nash outcomes are always more profitable
under IQ management regardless of the number of subjects who chose it.
On the basis of their prior experience, subjects chose to retain
the 3,000 pounds of quota associated with them as an individual property
right in 423 of the 540 instances (78.3% of the time). (7) This is
different from random with p < [10.sup.-38]. Exactly half--54 of
108--of the subjects never chose CP management.
This is a strong preference for IQ management, but the persistence
of a non-negligible number of subjects competing in a CP is puzzling. It
also reflects an important element of real catch share programs, as in
each of the cases discussed in Section I a significant number of
harvesters chose not to join sectors when given the chance, often citing
philosophical opposition to allocating property rights to the fishery.
However, that is an unlikely motive among a selection of college student
subjects. To understand the factors that contribute to a subject's
choice to join a CP management group, Table 4 presents the results of a
series of random effects logit models of management system choice, with
the IQ alternative coded as one.
Economic theory would suggest that subjects' decisions should
be determined by the subject's relative profitability under IQ and
CP management. However, the InMaxProfitDif variable, which captures the
difference in each subject's maximum profit in the IQ treatment and
in the CP treatment, is not a significant factor in explaining variation
in the choice of management in the Choice treatment. (8) Instead, the
model identifies a series of lagged variables as significant
determinants of the choice. A positive and significant coefficient on
[NOthers.sub.t-1], the number of other subjects who chose CP in the last
season, means subjects avoid larger CP in all models. The lagged
dependent variable, [CPChosen.sub.t-1], is significant in Model 1 and
indicates subjects who chose CP in the past are more likely to do it
again, even controlling for the significant effect of past catch in the
CP, [CPCatchi.sub.t-1]. However, measuring past catch instead by
[CPOver3K.sub.t-1], a dummy variable for whether the subject was able to
catch more than her equal 3,000 pound quota share last time she/he was
in the CP group, swamps the significance of the lagged dependent
variable. Adding another measure of the subject's aptitude under CP
management, a significant negative coefficient on [CPMaxCatchMix.sub.i]
indicates subjects who earn more in CP are more likely to choose it,
independent of whether they are also better at IQ. Collectively, this
model suggests that people who choose CP management are more likely to
be "successful" in CP management, but they are defining
success with respect to quantity landed rather than relative
profitability.
V. DISCUSSION
To address an emerging generation of dynamic common pool resource
(CPR) policy questions about the distribution of effort during a season
induced by a fixed TAC which successfully manages interseason stock
externalities we designed a novel dynamic CPR experimental environment
in which subjects were playing the role of harvesters managed by either
CP or IQ management. In the single management treatments, the data are
broadly consistent with Nash equilibrium: there is a race-to-fish under
CP management, and the high landings result in low prices and low
profits; IQ-managed subjects distribute their landings throughout the
season, receiving higher prices and earning higher profits. When
subjects were split among IQ and CP management, the key features
predicted by Nash equilibrium were again observed. First, the CP
subjects still raced to fish, essentially unaffected by the presence of
the IQ subjects. However, the IQ subjects limited their effort during
the derby, and increased it significantly once the CP fishery was
closed.
This strategic response of the IQ subjects to the presence of CP
subjects is useful to observe in the laboratory for several reasons.
First, that the Nash prediction is realized supports using our model as
a framework for organizing the incentives of competing IQ and CP groups,
and thus if the same incentives present themselves in the field, we
would expect to observe similar comparative statics in actual harvester
behavior. Second, from a management perspective, this change in effort
carries practical importance: harvesters may have alternative fisheries
they can pursue when they are not pursuing the sector-managed fishery,
and thus the change to sector management can influence the degree of
effort in other fisheries. That is, the IQ-managed harvesters who
previously had to pursue the sector-managed fish during derbies can now
find other species to pursue during those times. Scheld et al. (2012)
found this is exactly what RI fluke sector vessels did, putting greater
effort on skate and dogfish during the CP fluke derbies.
Our model and laboratory environment relies on perfect enforcement,
ensuring individual vessels and groups of vessels stop fishing when the
quota is met. Catch share management like that evaluated here has been
adopted primarily in countries with functional governments, and
resources for tracking and enforcing fishing activity at the vessel
level (Melnychuk et al. 2012). Our motivating cases are large commercial
fisheries in US territorial waters, where there is an extensive
combination of on-board observers and dealer-based landings reporting to
ensure catch is reported and counted against quota. While there are
violations of fishing regulations, they do not alter the daily price
externality (or interseason stock externality) to an economically
significant extent. Thus, we expect our results to be broadly
informative about the behavior and policy value in our applications of
interest.
One of the major policy questions arising out of the initial
experiences with catch share and sector programs is how they will
expand, evolve, or fade away. The evolution of institutions has been a
subject of recent experiments on the rule of law (e.g., Gurerk,
Irlenbusch, and Rockenbach 2006). The RI fluke sector pilot grew through
2009-2011, as few people want to leave the sector, but others want to
join; but the program was discontinued by the RI Department of
Environmental Management in 2012. Similarly, the Chignik Coop attracted
additional members, until courts declared its arrangement
unconstitutional. The Cape Cod Hook and Cape Cod Fixed Gear sectors have
seen their concept expanded throughout the Multispecies fishery, despite
the resistance in the fishery to move to catch share management through
the declines of the previous 30 years.
The Choice treatment allowed subjects to choose their management
system based on what they had experienced and observed within each
system. A strong preference for rights-based management emerged,
suggesting that successful management groups will be able to attract
more members, and to the extent that IQ management leads to success, IQ
management will expand throughout the fishery. Viewed through this lens,
sector-based catch share programs are not just a management tool, but a
political tool for implementing effective management. They establish a
process through which users can select the management methods that best
suit their businesses, without needing to persuade regulators that they
will be effective at anything other than keeping within the allocated
catch share. If indeed they are effective, then the sector and its
management will grow to be an important part of the fishery's
management portfolio.
However, it is also often true that these sector programs were
established to strike a political compromise between a subset of the
industry interested in greater control over their harvesting than was
afforded by current management, and by other participants who opposed
individual quota allocation, or rights-based management altogether. In
some cases, the sector groups were very large and held an overwhelming
majority of the quota (e.g., Northeast Multispecies sectors left less
than 5% of the quota for the CP; the Chignik Coop held over 80% of the
quota), whereas in others, most of the quota remained in the CP (e.g.,
in Rhode Island, the sector held 11% of the quota). This gives rise to a
potential future research question of how sectors with catch shares will
adopt different management strategies depending on their relative
dominance with respect to CP harvesters, and how the realized outcomes
differ from those in the competitive CP fishery.
ABBREVIATIONS
CP: Common Pool (experimental treatment)
CPR: Common Pool Resource
IQ: Individual Quota (experimental treatment)
ITQ: Individual Transferable Quota
TAC: Total Allowable Catch
doi: 10.1111/ecin.12057
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Appendix S1. Derivation of Nash Equilibrium Predictions
(1.) Summer flounder, or fluke, is a species that is jointly
harvested with the Northeast Multispecies groundfish complex in southern
New England, but is managed by the states even in federal waters.
(2.) This structure captures our motivating case of the RI fluke
sector, but also characterizes the US/Canadian halibut and
Virginia/North Carolina striped bass cases, and the Cape Cod sectors.
The Chignik Coop served primarily to reduce effort costs, which is not
well approximated by an individual quota sector.
(3.) Our choice of model setup that fisherman faces a downward
sloping demand is based on our motivating case of the Rhode Island fluke
fishery. See Scheld et al. (2012) for details.
(4.) Without the stock effect in the harvest or cost functions,
there is no consequence of today's harvest on future productivity
or cost, thus the temporal linkage with regard to harvest decisions does
not exist. Furthermore, zero discounting means profit in each period has
equal weight. Thus, the harvest will be the same in every period: if
landings are lower in one period than another, a harvester could receive
higher price by shifting her landings from a higher total landings (and
lower price) period to the lower total landings period. Fell (2009)
relaxes these assumptions, but must find Nash equilibrium numerically.
(5.) Any distribution of effort across individuals through time
that leads to constant landings will satisfy the Nash equilibrium
conditions. This model is formulated with symmetric agents, which makes
it natural to think the symmetric equilibrium will be selected among the
set of equilibrium strategies, but there is no equilibrium or refinement
reason to expect so. (In fact, our experimental data show considerable
asymmetry and effort level switching during a season, though with
aggregate levels consistent with Nash equilibrium.)
(6.) For the parameters used in the experiment, it is strictly more
profitable to be in the IQ managed sector than the CP managed sector in
Nash equilibrium, regardless of the number of other subjects who choose
IQ. The only exception is when all but one subject chooses IQ, in which
case the lone CP subject will effectively have an IQ.
(7.) CP management was selected 117 times, though in one season,
only one subject in the session chose CP, and thus effectively had an
individual quota.
(8.) A wide range of other profit variables, including gross profit
levels in the Mixed treatment, and the difference in average or maximum
profit in the IQ and CP treatments were similarly insignificant.
CHRISTOPHER M. ANDERSON and HIROTSUGU UCHIDA *
* The authors are grateful to James Nugent for developing the
experimental software, and to Mihoko Tegawa for research assistance.
This work was supported by Rhode Island Sea Grant(#NA080AR4170691) and
Rhode Island Agricultural Experiment Station (AES#5336).
Anderson: Associate Professor, School of Aquatic and Fishery
Sciences, University of Washington, Seattle. WA 98195. Phone
1-(206)-543-1101, Fax 1-(206)-685-7471, E-mail cmand@uw.edu
Uchida: Assistant Professor, Department of Environmental &
Natural Resource Economics, 205 Kingston Coastal Institute, University
of Rhode Island, Kingston. RI 02881. Phone 1-(401)-874-2238, Fax
1-(401)-874-2156. E-mail uchida@uri.edu
TABLE 1
Nash Equilibrium Strategies for the CP,
IQ, and Mixed Treatments
Mean Nash
Treatment Weeks Days Nash Profit
All CP
12 CP 1-14 7 $10,576
subjects 15 2
16-52 Closed
All IQ
12 IQ 1-52 1.92 $75,077
Mixed
6 CP 1-14 7 $55,180
15 2
16-52 Closed
6 IQ 1-14 0 $82,619
15 0.68
16-52 2.68
TABLE 2
Mixed-Effects Model of Total Effort in the
Last Season of the CP and IQ Treatments
Variable Coefficient
Constant 61.94
(51.34) ***
IQ -29.77
(20.69) ***
Week 0.35
(4.53) ***
Experience 2.56
(0.67)
IQ x Week -0.68
(8.70) ***
IQ x Experience -14.91
(3.30) ***
Week x Experience 0.33
(1.18)
Week x Experience x IQ 0.14
(0.50)
LnL = -5549.65 N = 1651
Wald [chi square] = 2323.45
Notes: Z statistics in parentheses. *** Significant at 1%.
TABLE 3
Seemingly Unrelated Regressions of Total Effort
in the Mixed Treatment
Variable CP IQ
Constant 31.77 10.96
(23.10) *** (13.52) ***
CP_Closure -31.77 11.44
(23.10) (7.26) ***
Week -0.02 0.02
(0.21) (0.37)
Experience 4.28 -6.60
(0.97) (4.79) ***
CP_Closure x Week 0.02 -0.26
(0.21) (3.85) ***
CP_Closure x -4.28 7.22
Experience (0.97) (1.22)
Week x Experience -0.08 0.17
(0.21) (-1.62)
Week x Experience x 0.08 -0.12
CP_Closure (0.21) (0.48)
N = 1656
Pseudo-[R.sup.2] 0.951 0.198
Note: Z statistics are in parentheses. Significant
at *** 1% level.
TABLE 4
Random Effects Logit Model of Choice of IQ in Choice Treatment
Model 1 Model 2 Model 3
Constant 10.73 2.33 4.96
(3.72) *** (2.22) *** (3.11) ***
InMaxProfitDif 0.07 0.07 0.09
(1.12) (1.10) (1.39)
[NOthers.sub.t-1] 0.35 0.38 0.38
(2.30) (2.50) ** (2.48) **
[CPChosen.sub.t-1] -8.07 0.243
(3.00) *** (0.38)
[CPCatchi.sub.t-1] -0.003
(3.85) ***
[CPOver3K.sub.t-1] -2.81 -2.77
(3.71) *** (4.70) ***
[CPMaxCatchMix.sub.i] -0.0006
(1.84) *
N = 432
InL -144.20 -150.19 -148.37
Wald [chi square] 33.81 29.66 32.28
Note: Z statistics are in parentheses. Significant at *** 1%, ** 5%,
and * 10% levels.